Parameter data sharing for multi-learner training of machine learning applications

ABSTRACT

A machine receives a first set of global parameters from a global parameter server. Multiple learner processors in the machine execute an algorithm that models an entity type using the first set of global parameters and a mini-batch of data known to describe the entity type. The machine generates a consolidated set of gradients that describes a direction for the first set of global parameters in order to improve an accuracy of the algorithm in modeling the entity type when using the first set of global parameters and the mini-batch of data. The machine transmits the consolidated set of gradients to the global parameter server. The machine then receives a second set of global parameters from the global parameter server, where the second set of global parameters is a modification of the first set of global parameters based on the consolidated set of gradients.

BACKGROUND

The present invention relates to the field of computers, andparticularly to computers that are capable of supporting machinelearning. Still more particularly, the present invention relates toproviding parameters needed in machine learning to learning machines.

SUMMARY

In one or more embodiments of the present invention, acomputer-implemented method enables parameter data sharing. A machinereceives a first set of global parameters from a global parameterserver. The first set of global parameters includes data that weightsone or more operands used in an algorithm that models an entity type.Multiple learner processors in the machine execute the algorithm usingthe first set of global parameters and a mini-batch of data known todescribe the entity type. The machine generates a consolidated set ofgradients that describes a direction for the first set of globalparameters in order to improve the accuracy of the algorithm in modelingthe entity type when using the first set of global parameters and themini-batch of data known to describe the entity type. The machinetransmits the consolidated set of gradients to the global parameterserver. The machine then receives a second set of global parameters fromthe global parameter server, where the second set of global parametersis a modification of the first set of global parameters based on theconsolidated set of gradients.

The described invention may also be implemented in a computer systemand/or as a computer program product.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary system and network in which the presentdisclosure may be implemented;

FIG. 2 illustrates a high level overview of machine learning as used inone or more embodiments of the present invention;

FIG. 3 illustrates a gradient graph as used in one or more embodimentsof the present invention;

FIG. 4 depicts an overview of one or more embodiments of the presentinvention that utilize central processing units (CPUs);

FIG. 5 depicts an overview of one or more embodiments of the presentinvention that utilize graphics processing units (GPUs);

FIG. 6 is a high-level flow chart illustrating a process in accordancewith one or more embodiments of the present invention;

FIG. 7 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 8 depicts abstraction model layers of a cloud computer environmentaccording to an embodiment of the present invention.

DETAILED DESCRIPTION

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

With reference now to the figures, and in particular to FIG. 1 , thereis depicted a block diagram of an exemplary system and network that maybe utilized by and/or in the implementation of the present invention.Some or all of the exemplary architecture, including both depictedhardware and software, shown for and within computer 101 may be utilizedby software deploying server 149 and/or learner machines 151 shown inFIG. 1 , and/or global parameter server 401 and/or machines 451-A and451-B shown in FIG. 4 , and/or global parameter server 501 and/ormachines 551-A and 551-B shown in FIG. 5 .

Exemplary computer 101 includes one or more processor(s) 103 that arecoupled to a system bus 105. Processor(s) 103 may each utilize one ormore core(s) 123, which contain execution units and other hardwarebeyond that found in the rest of the processor(s) 103 (e.g., on-boardrandom access memory, etc.). A video adapter 107, which drives/supportsa display 109 (which may be a touch-screen display capable of detectingtouch inputs onto the display 109), is also coupled to system bus 105.System bus 105 is coupled via a bus bridge 111 to an input/output (I/O)bus 113. An I/O interface 115 is coupled to I/O bus 113. I/O interface115 affords communication with various I/O devices, including a keyboard117, a mouse 119, a media tray 121 (which may include storage devicessuch as CD-ROM drives, multi-media interfaces, etc.), and external USBport(s) 125. While the format of the ports connected to I/O interface115 may be any known to those skilled in the art of computerarchitecture, in one embodiment some or all of these ports are universalserial bus (USB) ports.

As depicted, computer 101 is able to communicate with a softwaredeploying server 149 and/or other devices/systems, such as systems thatsupport the depicted learner machines 151, using a network interface129. Network interface 129 is a hardware network interface, such as anetwork interface card (NIC), etc. Network 127 may be an externalnetwork such as the Internet, or an internal network such as an Ethernetor a virtual private network (VPN). In one or more embodiments, network127 is a wireless network, such as a Wi-Fi network, a cellular network,etc.

A hard drive interface 131 is also coupled to system bus 105. Hard driveinterface 131 interfaces with a hard drive 133. In one embodiment, harddrive 133 populates a system memory 135, which is also coupled to systembus 105. System memory is defined as a lowest level of volatile memoryin computer 101. This volatile memory includes additional higher levelsof volatile memory (not shown), including, but not limited to, cachememory, registers and buffers. Data that populates system memory 135includes computer 101's operating system (OS) 137 and applicationprograms 143.

OS 137 includes a shell 139, for providing transparent user access toresources such as application programs 143. Generally, shell 139 is aprogram that provides an interpreter and an interface between the userand the operating system. More specifically, shell 139 executes commandsthat are entered into a command line user interface or from a file.Thus, shell 139, also called a command processor, is generally thehighest level of the operating system software hierarchy and serves as acommand interpreter. The shell provides a system prompt, interpretscommands entered by keyboard, mouse, or other user input media, andsends the interpreted command(s) to the appropriate lower levels of theoperating system (e.g., a kernel 141) for processing. While shell 139 isa text-based, line-oriented user interface, the present invention willequally well support other user interface modes, such as graphical,voice, gestural, etc.

As depicted, OS 137 also includes kernel 141, which includes lowerlevels of functionality for OS 137, including providing essentialservices required by other parts of OS 137 and application programs 143,including memory management, process and task management, diskmanagement, and mouse and keyboard management.

Application programs 143 include a renderer, shown in exemplary manneras a browser 145. Browser 145 includes program modules and instructionsenabling a world wide web (WWW) client (i.e., computer 101) to send andreceive network messages to the Internet using hypertext transferprotocol (HTTP) messaging, thus enabling communication with softwaredeploying server 149 and other systems.

Application programs 143 in computer 101's system memory (as well assoftware deploying server 149's system memory) also include a Programfor Training and Utilizing Machine Learning Applications (PTUMLA) 147.PTUMLA 147 includes code for implementing the processes described below,including those described in FIGS. 2-6 . In one embodiment, computer 101is able to download PTUMLA 147 from software deploying server 149,including in an on-demand basis, wherein the code in PTUMLA 147 is notdownloaded until needed for execution. In one embodiment of the presentinvention, software deploying server 149 performs all of the functionsassociated with the present invention (including execution of PTUMLA147), thus freeing computer 101 from having to use its own internalcomputing resources to execute PTUMLA 147.

Also within computer 101 in one or more embodiments are one or moregraphics processing units depicted as GPU(s) 153. A graphics processingunit (GPU) is a processing device that is specifically architected tohandle large strings of data, which are often associated with a graphicsdisplay. That is, a GPU is designed to handle strings of data that areused to control the appearance of pixels on a display. One or moreembodiments of the present invention utilize GPU(s) 153 not only toanalyze data associated with pixels in a display, but are also used toanalyze any strings of data used to describe a particular type ofentity, as described below.

The hardware elements depicted in computer 101 are not intended to beexhaustive, but rather are representative to highlight essentialcomponents required by the present invention. For instance, computer 101may include alternate memory storage devices such as magnetic cassettes,digital versatile disks (DVDs), Bernoulli cartridges, and the like.These and other variations are intended to be within the spirit andscope of the present invention.

With reference now to FIG. 2 , a high level overview of machine learningas used in one or more embodiments of the present invention is depictedas machine learning 200.

The goal of machine learning 200 is to generate a uniform output 202from a computation 204 using input 206 and parameter(s) 208.

Computation 204 is an execution of a mathematical algorithm using theinput 206 and the parameter(s) 208.

Input 206 is a set of data that describes a type of entity.

Parameter(s) 208 are weights for various components of input 206.

Output 202 is the result of executing the algorithm in computation 204.

For example, assume that the following algorithm is used in computation204:

A+B=X

Assume now that A is data that describes an animal's whiskers, and thatB is data that describes an animal's tail. If X=1, then the input 206(A, B) is data that, when applied to the algorithm A+B, describes a cat.However, if X=2, then the input 206 (A, B) is data that, when applied tothe algorithm A+B, describes a dog.

That is, assume that A describes how long the animal's whiskers are andB describes how long the animal's tail is. Thus, if (A+B) is (0.4+0.6),then X=1.0, and the values for A (0.4) and B (0.6) are indicative of acat. However, if (A+B) is (0.8+1.2), then X=2.0, and the values for A(0.8) and B (1.2) are indicative of a dog.

In practical applications, however, the values for A and B are notuniform in value and/or importance. That is, the input 206 (A, B) mayactually be 0.8 and 1.2 (instead of 0.4 and 0.6), and yet still be froma descriptor/measurement of the values associated with a particularcat's whiskers (0.8) and tail (1.2), even though X is now equal to 2(0.8+1.2).

Furthermore, there are often hundreds, if not millions, of data units ininput 206 used to describe a cat or a dog.

Therefore, parameters 208 are used to weight these data units.

Returning to the illustrative algorithm of A+B=X to determine whetherthe data describes a cat or a dog, assume now that the values of A and Bcome from a set of training files, known as a “mini-batch”. That is, afirst mini-batch is a set of data that is known to describe a particulartype of entity, such as a cat. However, the values of A and B within thefirst mini-batch will differ.

For example, in a first set of data from the first mini-batch, assumethat the respective values for A and B are 2 and 4 (i.e., A=2 and B=4).In a second set of data from the first mini-batch, assume that therespective values for A and B are 3 and 5 (i.e., A=3 and B=5). In athird set of data from the first mini-batch, assume that the respectivevalues for A and B are 1 and 5 (i.e., A=1 and B=5). Using these valuesunweighted (i.e., without parameters/parameter data/parameter values)would result in the formula A+B=X giving the results of.

2+4=6 (for the first set of data from the first mini-batch)

3+5=8 (for the second set of data from the first mini-batch)

1+5=6 (for the third set of data from the first mini-batch)

Without any parameter weights, none of these data will result in a valueof 1.0 for X (indicative of a cat). Therefore, an initial set ofparameters (also referred to herein as weights, parameter weights,parameter data, parameter values, etc.) is applied to the data, in orderto force X to be close to the value of 1.

In an embodiment of the present invention, a same parameter value isapplied to each of the input data. For example, assume that theparameter/parameter value is 0.2 and is to be multiplied against each ofthe data values. In the example shown above, this would result in:

2*0.2+4*0.2=0.4+0.8=1.2 (for the first set of data from the firstmini-batch)

3*0.2+5*0.2=0.6+1.0=1.6 (for the second set of data from the firstmini-batch)

1*0.2+5*0.2=0.2+1.0=1.2 (for the third set of data from the firstmini-batch)

This brings the average value of X near 1.0, thus indicating that thedata describes a cat.

In an embodiment of the present invention, assume that a secondmini-batch is of data known to describe a dog. The algorithm used toidentify a dog may be a different algorithm than A+B=X (used to identifya cat). However, for purposed of illustration, assume that the samealgorithm (A+B=X) and parameter (0.2) is used with “dog” data in thesecond mini-batch.

For example, in a first set of data from the second mini-batch, assumethat the respective values for A and B are 4 and 8 (i.e., A=4 and B=8).In a second set of data from the second mini-batch, assume that therespective values for A and B are 6 and 10 (i.e., A=6 and B=10). In athird set of data from the second mini-batch, assume that the respectivevalues for A and B are 1 and 5 (i.e., A=1 and B=5).

Assume now that the same parameter/parameter value of 0.2 is multipliedagainst each of the data values. In the “dog” example, this would resultin:

4*0.2+8*0.2=0.8+1.6=2.4 (for the first set of data from the secondmini-batch)

6*0.2+10*0.2=1.2+2.0=3.2 (for the second set of data from the secondmini-batch)

2*0.2+10*0.2=0.4+2.0=2.4 (for the third set of data from the secondmini-batch)

This brings that average value of X near 2.0, thus indicating that thedata describes a dog.

In one or more embodiments of the present invention, however, differentparameters are applied to each of the data values in the sets of data.For example, consider again the algorithm of A+B=X presented above, andassume that the data is the same known data that describes a cat aspresented above. However, instead of applying the same parameter to eachdatum within the data set, a different parameter is applied to eachdatum. For example, assume that the parameter 0.3 is multiplied againstA and the parameter 0.1 is applied against B.

Using the values for A and B in the example shown above for known “cat”data, this would result in:

2*0.3+4*0.1=0.6+0.4=1.0 (for the first set of data from the firstmini-batch)

3*0.3+5*0.1=0.9+0.5=1.4 (for the second set of data from the firstmini-batch)

1*0.3+5*0.1=0.3+0.5=0.8 (for the third set of data from the firstmini-batch)

This brings that gradient of values for X closer 1.0, thus providing amore accurate indication that the data describes a cat.

With reference now to FIG. 3 , a gradient graph 300 illustrates theconcept of a gradient, and particularly the gradient of the value of Xin the algorithm A+B=X. As shown in gradient graph 300, assume thatmultiple mini-batches have been run using a particular set of parameters(e.g., a global parameter of 0.2 or discrete parameters of 0.3 and 0.1as described above). As shown in gradient graph 300, there is a heavyconcentration of values for X at 1.0, indicating that the parameter(s)used when executing the algorithm A+B (and using the data from themini-batch as inputs) were a good choice, even though there are stilloutlier results at 0.7 and 1.4.

With reference now to FIG. 4 , an overview of one or more embodiments ofthe present invention that utilize central processing units (CPUs) ispresented.

As depicted in FIG. 4 , a global parameter server 401 (analogous inarchitecture to computer 101 shown in FIG. 1 ) supplies globalparameters used by machine 451-A and machine 451-B (which are analogousto the learner machines 151 shown in FIG. 1 ).

Assume that CPU 403 a-1 and CPU 403 a-2 initially were trained usinglocal parameters. As suggested by their identifiers, CPU 403 a-1 is partof learner 441 in machine 451-A, and CPU 403 a-2 is part of learner 442in machine 451-A.

Assume that CPU 403 a-1 was fed data from a mini-batch of data known todescribe a cat and CPU 403 a-2 was fed data from the same or a differentmini-batch of data known to describe a cat. CPU 403 a-1 and CPU 403 a-2will initially use initial local parameters 406 a (e.g., parameters thatare stochastically generated (randomly selected) for use by CPU 403 a-1and CPU 403 a-2). For example, CPU 403 a-1 and CPU 403 a-2 may both usethe parameters “0.3” and “0.1” to be multiplied against the respectivevalues of A and B in the algorithm A+B=X described above. These valuesfor the initial local parameters 406 a may be generated by one or bothof the CPU 403 a-1 and CPU 403 a-2, or by another processor (not shown).

Gradients memory 404 a describes how to improve “X” to 1.0 when theinitial local parameters 406 a were used when evaluating mini-batch datathat was known to describe a cat. As shown in FIG. 4 , these initiallocal parameters 406 a may be sent to global parameter server 401. Thatis, the gradients (such as those depicted in FIG. 3 ) for “X” thatresulted from using “0.3” and “0.1” as the initial local parameters 406a are sent to global parameter server 401.

Thus, the initial local parameters 406 a are sent to global parameterserver 401 as a “starting point” set of parameters, which will bemodified by the global parameter server 401 based on the gradients 404 athat resulted from the use of these initial local parameters 406 a byCPU 403 a-1 and CPU 403 a-2.

Similarly, initial local parameter 406 b and gradients memory 404 bgenerated by CPU 403 b-3 in learner 443 and CPU 403 b-4 in learner 444in machine 451-B are sent to global parameter server 401.

The global parameter server 401 will thus 1) consolidate (e.g., average)the initial local parameters 406 a and initial local parameters 406 b,and 2) modify them according to the gradients 404 a and gradients 404 bthat resulted in respective machine 451-A and machine 451-B, in order tocreate a global parameter. As shown in FIG. 4 , a copy of the sameglobal parameter is stored in a global parameters memory 408 a andglobal parameters memory 408 b in respective machines 451-A and 451-B.

Global parameters memory 408 a is a shared memory from which CPU 403 a-1and CPU 403 a-2 are both able to read the global parameters receivedfrom global parameter server 401. Likewise, global parameters memory 408b is a shared memory from which CPU 403 b-3 and CPU 403 b-4 are bothable to read the global parameters received from global parameter server401. Thus, the bandwidth consumption between global parameter server 401and machine 451-A and between global parameter server 401 and machine451-B is greatly reduced, since CPU 403 a-1 and CPU 403 a-2 are able toshare the same parameters found in global parameters memory 408 a (andsince CPU 403 b-3 and CPU 403 b-4 are able to share the same parametersfound in global parameters memory 408 b).

After using the global parameter(s) from global parameter server 401when applying an algorithm (e.g., A+B=X) to data from a mini-batch, CPU403 a-1 and CPU 403 a-2 send their respective gradients (of theresulting values “X”) to gradients memory 404 a. These gradients may beconsolidated (e.g., averaged) by machine 451-A in order to furtherreduce bandwidth consumption between global parameter server 401 andmachine 451-A, or (alternatively) each of the gradients may sent frommachine 451-A to global parameter server 401. Similarly, gradientsresulting from the use of global parameter(s) by machine 451-B will besent to global parameter server 401.

The process reiterates such that each time the learners 441, 442, 443,and/or 444 use a new global parameter against a same or different set oflearning data (e.g., mini-batches of data that describes a known type ofentity such as a cat), new gradients are generated and sent to theglobal parameter server 401, which then further tweaks (i.e., predicts anext iteration of) the global parameter.

As shown in FIG. 4 , the gradients memory 404 a and global parametersmemory 408 a are shared memory that is shared between CPU 403 a-1 andCPU 403 a-2 (just as gradients memory 404 b and global parameters memory408 b are shared memory that is shared among CPU 403 b-3 and CPU 403b-4). Thus, gradients memory 404 a and global parameters memory 408 aperform the function of a local parameter server 402 a (just asgradients memory 404 b and global parameters memory 408 b perform thefunction of a local parameter server 402 b). That is, rather than CPU403 a-1 and CPU 403 a-2 having to pull duplicate copies of the updatedparameters from the global parameter server 401 (and thus consuming alarge amount of bandwidth in the network between the global parameterserver 401 and the machine 451-A), a single copy of the updatedparameters is sent to the global parameters memory 408 a within themachine 451-A, which is then stored in the shared memory (that is,global parameters memory 408 a) for use by CPU 403 a-1 and CPU 403 a-2.Similarly, a single copy of the updated parameters is sent to the globalparameters memory 408 b within the machine 451-B, which is then storedin the shared memory (that is, global parameters memory 408 b) for useby CPU 403 b-3 and CPU 403 b-4.

While FIG. 4 has shown the present invention implemented in a CPU-basedset of machines 451-A and 451-B, in another embodiment the presentinvention is implemented in a graphics processing unit (GPU) basedsystem, as depicted in FIG. 5 .

As depicted in FIG. 5 , a global parameter server 501 (analogous toglobal parameter server 401 shown in FIG. 4 ) supplies global parametersused by machine 551-A and machine 551-B (which are analogous to thelearner machines 151 shown in FIG. 1 ).

However, rather than utilizing CPUs, machines 551-A and 551-B utilizegraphic processing units (GPUs) 553 a-1, 553 a-2, 553 b-3, and 553 b-4.These GPUs are specialized processors that were originally designed tocontrol a display. More specifically, GPUs are designed to handle arraysof pixel data, which then controls a display. For example, a graphicsarray may contain data describing the hue, intensity, etc. of each pixelin a display. As suggested by their identifiers, GPU 553 a-1 is part oflearner 541 in machine 551-A, GPU 553 a-2 is part of learner 542 inmachine 551-A, GPU 553 b-3 is part of learner 543 in machine 551-B, andGPU 553 b-4 is part of learner 544 in machine 551-B (analogous,respectively, to learners 441, 442, 443, and 444 in FIG. 4 ).

As shown in FIG. 5 , the gradients memories 504 a-504 b, initial localparameters 506 a-506 b, and global parameters memories 508 a-508 b arestored in random access memory (RAM) 535 a-535 b in the respectivemachines 551-A and 551-B.

Thus, RAM 535 a and RAM 535 b provide shared memories from which globalparameters are made available to GPUs 553 a-1, 553 a-2, 553 b-3, and 553b-4 when testing the mini-batches against the algorithms describedabove. The system shown in FIG. 5 provides the advantage of savingbandwidth between global parameter server 501 and machines 551-A and551-B and increased efficiency from using local memories to retrieve theparameters (as in FIG. 4 ), plus provides the performance improvementover the system shown in FIG. 4 by using GPUs whose performance may bebetter suited than CPUs for the presently described operations (due totheir ability to handle large quantities of vector data as found in themini-batches).

In one or more embodiments of the present invention, a singlethread/process is set up to download parameters from the globalparameter server 401/501 to the machines shown in FIGS. 4-5 . If thenetwork connecting global parameter server 401/501 to the machines shownin FIGS. 4-5 is the Internet or a similar IP-based network, then atransfer control protocol (TCP) is used to connect the systems. However,if a direct connection is established between the systems (e.g., anEthernet), then a remote direct memory access (RDMA) protocol can beused to directly “inject” the parameters into the global parametersmemories shown in FIGS. 5-6 .

Various processes can be used to consolidate parameters and/orgradients, either at the global parameter server 401/501, or within themachines shown in FIGS. 4-5 . For example, when the original parameteris derived from the initial local parameters 406 a-406 b (and/or 506a-506 b) by the global parameter server 401/501, or when the resultingglobal parameters are updated, these parameters can be mathematicallyaveraged, or they may be developed using the gradients from the gradientmemories described herein. That is, the global parameter server 401/501can create the updated global parameter using the gradients and adownpour stochastic gradient descent (SGD) algorithm, an elasticaveraging stochastic gradient descent (EASGD) algorithm, etc. that willprovide direction for speculating on how the global parameter should bechanged/updated.

With reference now to FIG. 6 , a high-level flow chart illustrating aprocess in accordance with one or more embodiments of the presentinvention is presented.

After initiator block 602, a first machine (e.g., machine 451-A shown inFIG. 4 ) receives a first set of global parameters from a globalparameter server (e.g., global parameter server 401), as described inblock 604. As described herein, the first set of global parametersincludes data that weights one or more operands used in an algorithmthat models an entity type. That is, the global parameter may be 0.3 and0.1 for the operands A and B in the algorithm A+B=C that models a cat(entity type).

As described in block 606, multiple learner processors (e.g., CPU 403a-1 and CPU 403 a-2) in the first machine execute the algorithm usingthe first set of global parameters and a first mini-batch of data knownto describe the entity type (e.g., a batch of vectors that are known todescribe a cat).

As described in block 608, the first machine generates a firstconsolidated set of gradients (stored in gradients memory 404 a eitheras a group or as a consolidated (e.g., averaged) group). The firstconsolidated set of gradients describes an accuracy of the algorithm inmodeling the entity type when using the first set of global parametersand the first mini-batch of data known to describe the entity type (seeFIG. 3 ).

As described in block 610, the first machine transmits the firstconsolidated set of gradients to the global parameter server.

As described in block 612, the first machine then receives a second setof global parameters from the global parameter server, where the secondset of global parameters is a modification of the first set of globalparameters based on the first consolidated set of gradients.

The flow chart ends at terminator block 614.

In an embodiment of the present invention, a second machine (e.g.,machine 451-B) also receives the first set of global parameters from theglobal parameter server, executes the algorithm using the first set ofglobal parameters and a second mini-batch of data known to describe theentity type, in order to generate a second consolidated set of gradientsthat describe the accuracy of the algorithm in modeling the entity typewhen using the first set of global parameters. The second machine thentransmits the second consolidated set of gradients to the globalparameter server, which creates a third set of global parameters, whichare received by and used by the first machine and the second machine.This third set of global parameters is a modification of the first setof global parameters based on the first consolidated set of gradientsand the second consolidated set of gradients.

Once the global parameters are established in final form (e.g., thethird set of global parameters), they can then be used against unknownsets of data to identify the subject of the data. That is, a thirdmachine (which may or may not be one of the machines 451-A or 451-B thatwere used to develop the final parameters) tests a set of unknown datausing the third set of global parameters in order to determine whetherthe set of unknown data matches the entity type. That is, by using theoptimized parameters with the algorithm described herein, the system isable to tell what type of entity (e.g., cat or dog) isrepresented/modeled by the data. This data may be purely descriptive(i.e., text-based descriptors) or graphic-based.

For example, the data may be a vector of image data of an animal. Byinputting this vector of image data (still or moving) into the algorithm(using the final parameters), the intelligent system is now able toidentity that animal as being a cat or a dog.

In an embodiment of the present invention and as described herein, adifferent learner processor (e.g., CPU 403 a-1 and CPU 403 a-2) in thefirst machine will generate each of the first consolidated set ofgradients. That is, CPU 403 a-1 will generate a first gradient and CPU403 a-2 will generate a second gradient. Each of the multiple learnerprocessors will then write their respective gradient to memory in thefirst machine (e.g., to gradients memory 404 a). The first machine willthen consolidate the different gradients (e.g., average them) in orderto create the first consolidated set of gradients. Otherwise, theconsolidated set of gradients is simply a collection of all of thegradients (as used in another embodiment of the present invention).

In an embodiment of the present invention, all of the multiple learnerprocessors in the first machine read the first set of global parametersand the second set of global parameters from a shared memory in thefirst machine (e.g., from global parameters memory 408 a).

In an embodiment of the present invention, one or more processors (e.g.,CPU 403 a-1) store global parameters currently in use by the firstmachine in a first memory in the first machine, and store globalparameters being downloaded from the global parameter server for futureuse in a second memory in the first machine. For example, assume thatglobal parameters memory 408 a is partitioned into two parts, whereinthe first partition is used to store global parameters that arecurrently being used and the second partition is used to store globalparameters that will be used in a next iteration. That is, the globalparameter server 401 can speculatively generate the next iteration ofglobal parameters, such that the machine 451-A can immediately startworking on a next (or the same) mini-batch after it has finishedtesting/using the current mini-batch using the current global parameter.

In order to determine if use of a global parameter is concluded, a useversion number or other indicator lets the learners (e.g., learners 441and 442) know that use of the current global parameter is completed.Version numbers can be used to characterize the staleness of parametersand can apply staleness-aware gradient update rule.

In an embodiment of the present invention, the system can use multiplememory buffers for continuous downloading of parameters from the remoteparameter server (e.g., global parameter server 401). Counters can beused to keep track of remaining users of local parameters and determinewhen the local parameter buffer can be reused.

In an embodiment of the present invention, global parameters furtherweight results from one or more particular operators used in thealgorithm that models the entity type.

For example, assume that the algorithm used to model the type of entityis:

A+(B*C)=X

As before, if X=1, then the data (A, B, C) describes a cat, while ifX=2, then the data describes a dog. However, the system may learn thatthe values that are multiplied (B and C) are inconsequential. As such,the results of any operation that uses operator*is discounted (e.g.,weighted such that it approaches zero). As a result, only theweighted/parameterized value A is used to generate X.

As described herein in one or more embodiments, the present inventionsets up local shared memory to hold local gradients (e.g., in gradientsmemory 404 a) and parameters (e.g., in global parameters memory 408 a).This provides local communication among the processors (e.g., CPUs 403a-1 and 403 a-2), allowing them to exchange gradients/parameters withina local parameter server (e.g., local parameter server 402 a) throughthis shared memory.

In an embodiment, the system uses an Inter-Process Communication (IPC)via the shared memory, which is mapped to each process's virtual memoryaddress space. That is, each parameter and/or gradient is mapped to avirtual memory address space for storage, which provides fault-isolationand fault-toleration compared to traditional thread-basedimplementations. This also offers the best possible IPC runtime speed,which is comparable to a thread-based solution.

In an embodiment of the present invention, the system sets up a localaggregation thread/process to aggregate gradients or local weights fromlocal learners, rather than sending individual gradients/weights to theglobal parameter server 401/501, thus further reducing bandwidthconsumption to the global parameter server 401/501.

The present invention may be implemented in one or more embodimentsusing cloud computing. Nonetheless, it is understood in advance thatalthough this disclosure includes a detailed description on cloudcomputing, implementation of the teachings recited herein is not limitedto a cloud computing environment. Rather, embodiments of the presentinvention are capable of being implemented in conjunction with any othertype of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 7 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-54Nshown in FIG. 7 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 8 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 7 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 8 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and parameter data sharing processing 96,which performs one or more of the features of the present inventiondescribed herein.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentinvention. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of various embodiments of the present invention has beenpresented for purposes of illustration and description, but is notintended to be exhaustive or limited to the present invention in theform disclosed. Many modifications and variations will be apparent tothose of ordinary skill in the art without departing from the scope andspirit of the present invention. The embodiment was chosen and describedin order to best explain the principles of the present invention and thepractical application, and to enable others of ordinary skill in the artto understand the present invention for various embodiments with variousmodifications as are suited to the particular use contemplated.

Any methods described in the present disclosure may be implementedthrough the use of a VHDL (VHSIC Hardware Description Language) programand a VHDL chip. VHDL is an exemplary design-entry language for FieldProgrammable Gate Arrays (FPGAs), Application Specific IntegratedCircuits (ASICs), and other similar electronic devices. Thus, anysoftware-implemented method described herein may be emulated by ahardware-based VHDL program, which is then applied to a VHDL chip, suchas a FPGA.

Having thus described embodiments of the present invention of thepresent application in detail and by reference to illustrativeembodiments thereof, it will be apparent that modifications andvariations are possible without departing from the scope of the presentinvention defined in the appended claims.

What is claimed is:
 1. A computer program product for parameter datasharing, the computer program product comprising: one or more computerreadable storage media, and program instructions collectively stored onthe one or more computer readable storage media, the programinstructions comprising: program instructions to receive a first set ofglobal parameters from a global parameter server, wherein the first setof global parameters are weights of one or more operands used in a firstalgorithm that models a first entity type, wherein each of the weightsused in the first algorithm has a same value as other weights used inthe first algorithm, and wherein the same value of the weights used inthe first algorithm is used for parameter weights in a second algorithmthat is based on the first algorithm to model a second entity type;program instructions to execute, by multiple learner processors, thefirst algorithm using the first set of global parameters and a firstmini-batch of data known to describe the first entity type; programinstructions to generate a first consolidated set of gradients thatdescribes a direction for the first set of global parameters in order toimprove an accuracy of the first algorithm in modeling the first entitytype when using the first set of global parameters and the firstmini-batch of data known to describe the first entity type; programinstructions to transmit the first consolidated set of gradients to theglobal parameter server; program instructions to receive a second set ofglobal parameters from the global parameter server, wherein the secondset of global parameters is a modification of the first set of globalparameters based on the first consolidated set of gradients, and whereinthe second set of global parameters is used in the second algorithm tomodel the second entity type; and program instructions to execute thesecond algorithm using the second set of global parameters to describethe second entity type.
 2. The computer program product of claim 1,wherein the multiple learner processors are hardware processors, andwherein the computer program product further comprises: programinstructions to generate each gradient from the first consolidated setof gradients by a different learner processor; program instructions towrite, by each of the multiple learner processors, each gradientgenerated by each of the multiple learner processors to a memory; andprogram instructions to consolidate gradients generated by all of themultiple learner processors in order to create the first consolidatedset of gradients.
 3. The computer program product of claim 1, furthercomprising: program instructions to read the first set of globalparameters and the second set of global parameters from a shared memory.4. The computer program product of claim 1, further comprising: programinstructions to store the first set of global parameters currently inuse when executing the first algorithm in a first memory; and programinstructions to store the second set of global parameters beingdownloaded from the global parameter server for future use, in a secondmemory, wherein the global parameters in the second set of globalparameters from the global parameter server are speculatively generatedglobal parameters, generated by the global parameter server, to be usedin a next iteration of the first algorithm.
 5. The computer programproduct of claim 1, wherein a first version number is assigned to thefirst set of global parameters, wherein a second version number isassigned to the second set of global parameters, and wherein thecomputer program product further comprises: program instructions toidentify all parameters from the first set of global parametersaccording to the first version number; program instructions to determinethat said all parameters from the first set of global parameters havebeen used to generate the first consolidated set of gradients based onsaid identifying all parameters from the first set of global parametersaccording to the first version number; and in response to determiningthat all parameters from the first global parameters have been used togenerate the first consolidated set of gradients, program instructionsto utilize the second set of global parameters, as identified by thesecond version number, to generate a second consolidated set ofgradients that further describes the direction for the first set ofglobal parameters.
 6. The computer program product of claim 1, whereinthe first consolidated set of gradients is an average of multiplegradients generated.
 7. The computer program product of claim 1, whereinthe first set of global parameters is a vector, wherein the multiplelearner processors are graph processing units (GPUs), and wherein thecomputer program product further comprises: program instructions togenerate the first consolidated set of gradients from the first set ofglobal parameters and the first mini-batch of data known to describe thefirst entity type.
 8. The computer program product of claim 1, furthercomprising: program instructions to determine that the one or moreoperands, used in the algorithm that models the entity type, comprise afirst operand and a second operand; program instructions to determinethat results of any operations that use a multiplication operator isinconsequential to modeling the first entity type; program instructionsto determine that the first operand is multiplied by the second operandin the first algorithm; and in response to determining that the firstoperand is multiplied by the second operand, program instructions toapply a weight that approaches zero to a product of the first operandand the second operand, wherein applying the weight that approaches zeroto the first operand removes the product of the first operand and thesecond operand from the algorithm before executing the first algorithm.9. The computer program product of claim 1, wherein the programinstructions are provided as a service in a cloud environment.
 10. Thecomputer program product of claim 1, wherein the computer programproduct further comprises: program instructions to receive the first setof global parameters from the global parameter server; programinstructions to execute the first algorithm using the first set ofglobal parameters and a second mini-batch of data known to describe thefirst entity type; program instructions to generate a secondconsolidated set of gradients that describe a direction for the firstset of global parameters in order to improve the accuracy of the firstalgorithm in modeling the first entity type when using the first set ofglobal parameters; program instructions to transmit the secondconsolidated set of gradients to the global parameter server; andprogram instructions to receive a third set of global parameters fromthe global parameter server, wherein the third set of global parametersis a modification of the first set of global parameters based on thefirst consolidated set of gradients and the second consolidated set ofgradients.
 11. A computer system, the computer system comprising: one ormore processors, one or more computer-readable memories, one or morecomputer-readable tangible storage medium, and program instructionsstored on the at least one of the one or more tangible storage mediumfor execution by at least one of the one or more processors via at leastone of the one or more memories, the stored program instructionscomprising: program instructions to receive a first set of globalparameters from a global parameter server, wherein the first set ofglobal parameters are weights of one or more operands used in a firstalgorithm that models a first entity type, wherein each of the weightsused in the first algorithm has a same value as other weights used inthe first algorithm, and wherein the same value of the weights used inthe first algorithm is used for parameter weights in a second algorithmthat is based on the first algorithm to model a second entity type;program instructions to execute, by multiple learner processors, thefirst algorithm using the first set of global parameters and a firstmini-batch of data known to describe the first entity type; programinstructions to generate a first consolidated set of gradients thatdescribes a direction for the first set of global parameters in order toimprove an accuracy of the first algorithm in modeling the first entitytype when using the first set of global parameters and the firstmini-batch of data known to describe the first entity type; programinstructions to transmit the first consolidated set of gradients to theglobal parameter server; program instructions to receive a second set ofglobal parameters from the global parameter server, wherein the secondset of global parameters is a modification of the first set of globalparameters based on the first consolidated set of gradients, and whereinthe second set of global parameters is used in the second algorithm tomodel the second entity type; and program instructions to execute thesecond algorithm using the second set of global parameters to describethe second entity type.
 12. The computer system of claim 11, wherein themultiple learner processors are hardware processors, and wherein thestored program instructions further comprise: program instructions togenerate each gradient from the first consolidated set of gradients by adifferent learner processor; program instructions to write, by each ofthe multiple learner processors, each gradient generated by each of themultiple learner processors to a memory; and program instructions toconsolidate gradients generated by all of the multiple learnerprocessors in order to create the first consolidated set of gradients.13. The computer system of claim 11, the stored program instructionsfurther comprising: program instructions to read the first set of globalparameters and the second set of global parameters from a shared memory.14. The computer system of claim 11, the stored program instructionsfurther comprising: program instructions to store the first set ofglobal parameters currently in use when executing the first algorithm ina first memory; and program instructions to store the second set ofglobal parameters being downloaded from the global parameter server forfuture use, in a second memory, wherein the global parameters in thesecond set of global parameters from the global parameter server arespeculatively generated global parameters, generated by the globalparameter server, to be used in a next iteration of the first algorithm.15. The computer system of claim 11, wherein a first version number isassigned to the first set of global parameters, wherein a second versionnumber is assigned to the second set of global parameters, and whereinthe stored program instructions further comprise: program instructionsto identify all parameters from the first set of global parametersaccording to the first version number; program instructions to determinethat said all parameters from the first set of global parameters havebeen used to generate the first consolidated set of gradients based onsaid identifying all parameters from the first set of global parametersaccording to the first version number; and in response to determiningthat all parameters from the first global parameters have been used togenerate the first consolidated set of gradients, program instructionsto utilize the second set of global parameters, as identified by thesecond version number, to generate a second consolidated set ofgradients that further describes the direction for the first set ofglobal parameters.
 16. The computer system of claim 11, wherein thefirst consolidated set of gradients is an average of multiple gradientsgenerated.
 17. The computer system of claim 11, wherein the first set ofglobal parameters is a vector, wherein the multiple learner processorsare graph processing units (GPUs), and wherein the stored programinstructions further comprise: program instructions to generate thefirst consolidated set of gradients from the first set of globalparameters and the first mini-batch of data known to describe the firstentity type.
 18. The computer system of claim 11, the stored programinstructions further comprising: program instructions to determine thatthe one or more operands, used in the algorithm that models the entitytype, comprise a first operand and a second operand; programinstructions to determine that results of any operations that use amultiplication operator is inconsequential to modeling the first entitytype; program instructions to determine that the first operand ismultiplied by the second operand in the first algorithm; and in responseto determining that the first operand is multiplied by the secondoperand, program instructions to apply a weight that approaches zero toa product of the first operand and the second operand, wherein applyingthe weight that approaches zero to the first operand removes the productof the first operand and the second operand from the algorithm beforeexecuting the first algorithm.
 19. The computer system of claim 11,wherein the program instructions are provided as a service in a cloudenvironment.
 20. The computer system of claim 11, the stored programinstructions further comprising: program instructions to receive thefirst set of global parameters from the global parameter server; programinstructions to execute the first algorithm using the first set ofglobal parameters and a second mini-batch of data known to describe thefirst entity type; program instructions to generate a secondconsolidated set of gradients that describe a direction for the firstset of global parameters in order to improve the accuracy of the firstalgorithm in modeling the first entity type when using the first set ofglobal parameters; program instructions to transmit the secondconsolidated set of gradients to the global parameter server; andprogram instructions to receive a third set of global parameters fromthe global parameter server, wherein the third set of global parametersis a modification of the first set of global parameters based on thefirst consolidated set of gradients and the second consolidated set ofgradients.